There are two general approaches to monitoring the pressure in vehicle tires: direct and indirect. Direct tire pressure monitoring systems typically comprise a wheel module having one or more sensors and electronics mounted in or to the tire to directly measure the tire's pressure and wirelessly transmit measurement data to the vehicle.
Indirect TPMSs generally utilize information from other vehicle sensors and/or systems to indirectly estimate a tire's pressure without direct TPMS sensors or electronics being located in the tire. Indirect TPMS is attractive because it can be more cost-efficient than direct TPMS. One conventional indirect TPMS uses wheel speed signals from the anti-lock brake system (ABS). For a typical passenger vehicle having four tires, the indirect TPMS compares the four wheel speed signals to determine whether a wheel is rotating faster because of a loss of pressure and related decreased diameter. One drawback to some of these indirect systems is that the systems cannot detect whether all wheels have lost pressure over time because the values are compared.
One approach for overcoming this drawback is to utilize a resonance frequency method (RFM) of analysis of a single resonance frequency in the sensed data signals from the ABS. U.S. Pat. Nos. 8,207,839 and 8,347,704 describe different kinds of RFM analysis of a time series of sensed data signals that includes auto-regression analysis, Fast Fourier analysis, a Bayesian analysis, or analysis based on a linear estimation model. While different kinds of analysis are taught by these patents, the purpose of each of these known RFM approaches is to reduce the amount of computation power required in an on-board processor to do the calculations necessary to identify a single resonance frequency from which tire pressure can be indirectly estimated. While RFM analysis can represent an improvement over conventional indirect TPMS, the accuracy of the results can be impacted by the low resolution of the ABS sensed data signals and by other factors that can influence the resonance frequency beyond just the tire pressure in an individual tire.
Therefore, in another approach, a multidimensional resonance frequency analysis (MRFA) that includes a spectral analysis identifying at least two tire vibration modes in the wheel speed signal and isolates at least one characteristic affecting the at least two tire vibration modes, for example, that described in U.S. patent application Ser. No. 13/919,620, which is herein incorporated by reference in its entirety, can also be utilized. In MRFA approaches, the number of tire parameters that can be extracted depends on the number of different resonance modes that can be identified in the spectrum of the wheel speed signal and on a significant difference of their respective dependence on the different parameters.
Moreover, typical direct TPMS and indirect TPMS do not interface with each other. Therefore, there is a need for combined direct tire pressure monitoring and tire resonance analysis for the extraction of tire characteristics in order to characterize other tire parameters.